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    Software for the integration of multiomics experiments in Bioconductor.

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    Authors
    Ramos, M
    Schiffer, L
    Re, A
    Azhar, R
    Basunia, A
    Rodriguez, C
    Chan, T
    Chapman, Phil
    Davis, S
    Gomez-Cabrero, D
    Culhane, A
    Haibe-Kains, B
    Hansen, K
    Kodali, H
    Louis, M
    Mer, A
    Riester, M
    Morgan, M
    Carey, V
    Waldron, L
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    Affiliation
    Graduate School of Public Health & Health Policy, City University of New York, New York, New York
    Issue Date
    2017-11-01
    
    Metadata
    Show full item record
    Abstract
    Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39-42. ©2017 AACR.
    Citation
    Software for the integration of multiomics experiments in Bioconductor. 2017, 77 (21):e39-e42 Cancer Res.
    Journal
    Cancer Research
    URI
    http://hdl.handle.net/10541/620717
    DOI
    10.1158/0008-5472.CAN-17-0344
    PubMed ID
    29092936
    Type
    Article
    Language
    en
    ISSN
    1538-7445
    ae974a485f413a2113503eed53cd6c53
    10.1158/0008-5472.CAN-17-0344
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

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